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the Hong Kong Polytechnic University
- https://scholar.google.com.hk/citations?hl=zh-CN&user=Xii74qQAAAAJ&view_op=list_works&sortby=pubdate
Stars
[CVPR 2024] MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Official codes of CCSRv2 and CCSRv1: Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
1.chatGPT注册 2.chatGPT成品项目整理 3.高效使用chatGPT的小技巧 ↓演示网站
A curated list of resources for Learning with Noisy Labels
Embedding Feature Whitening into Deep NeuralNetworks Optimizer
computer vision projects | 计算机视觉相关好玩的AI项目(Python、C++、embedded system)
Instantly improve your training performance of TensorFlow models with just 2 lines of code!
Distributed K-FAC Preconditioner for PyTorch
MNN applications by MNN, JNI exec, RK3399. Support tflite\tensorflow\caffe\onnx models.
Code for paper "Orthogonal Convolutional Neural Networks".
torch-optimizer -- collection of optimizers for Pytorch
OpenMMLab Detection Toolbox and Benchmark
Advanced optimizer with Gradient-Centralization
A New Optimization Technique for Deep Neural Networks
Papers for normalization techniques, released codes collections.
【CVPR2020, Interpretable Network】A Model-driven Deep Neural Network for Single Image Rain Removal
Variational Denoising Network: Toward Blind Noise Modeling and Removal (NeurIPS, 2019) (Pytorch)
Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet…
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels (CVPR, 2019) (PyTorch)
Code for the paper "Rethinking the CSC Model for Natural Images"
Learning warped guidance for blind face restoration (ECCV 2018)
PyTorch implementation of the TIP2017 paper "Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"
pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593